While numerous low-level computer vision problems such as denoising, deconvolution or optical flow estimation were traditionally tackled with optimization approaches such as proximal methods, recently deep learning approaches trained on numerous examples demonstrated impressive and sometimes superior performance on respective tasks. In my presentation, I will discuss recent efforts to bring together these seemingly different paradigms, showing how deep learning can profit from proximal methods and how proximal methods can profit from deep learning.

This presentation is part of Minisymposium “MS52 - A Denoiser Can Do Much More Than Just... Denoising (2 parts)”
organized by: Yaniv Romano (Technion - Israel Institute of Technology) , Peyman Milanfar (Google Research) , Michael Elad (The Technion - Israel Institute of Technology) .

Authors:

Daniel Cremers (Technische Universität München)

Tim Meinhardt (Technical University of Munich)

Thomas Frerix (Technical University of Munich)

Caner Hazirbas (Technical University of Munich)

Michael Moeller (University of Siegen)

Thomas Möllenhoff (Technical University of Munich)

Keywords:

deep learning, image enhancement, image reconstruction